There are a number of different entity extraction components, which can seem intimidating for new users.
Here we’ll go through a few use cases and make recommendations of what to use.

Component

Requires

Model

notes

ner_mitie

MITIE

structured SVM

good for training custom entities

ner_crf

crfsuite

conditional random field

good for training custom entities

ner_spacy

spaCy

averaged perceptron

provides pre-trained entities

ner_duckling

duckling

context-free grammar

provides pre-trained entities

The exact required packages can be found in dev-requirements.txt and they should also be shown when they are missing
and a component is used that requires them.

To improve entity extraction, you can use regex features if your entities have a distinctive format (e.g. zipcodes).
More information can be found in the Training Data Format.

Note

To use these components, you will probably want to define a custom pipeline, see Processing Pipeline.
You can add multiple ner components to your pipeline; the results from each will be combined in the final output.

spaCy has excellent pre-trained named-entity recognisers in a number of models. You can test them out in this awesome interactive demo. We don’t recommend that you try to train your own NER using spaCy, unless you have a lot of data and know what you are doing. Note that some spaCy models are highly case-sensitive.

The duckling package does a great job of turning expressions like “next Thursday at 8pm” into actual datetime objects that you can use. It can also handle durations like “two hours”, amounts of money, distances, etc. Fortunately, there is also a python wrapper for duckling! You can use this component by installing the duckling package from PyPI and adding ner_duckling to your pipeline.

In the introductory tutorial we build a restaurant bot, and create custom entities for location and cuisine.
The best components for training these domain-specific entity recognisers are the ner_mitie and ner_crf components.
It is recommended that you experiment with both of these to see what works best for your data set.

In the object returned after parsing there are two fields that show information about how the pipeline impacted the entities returned. The extractor field of an entity tells you which entity extractor found this particular entity. The processors field contains the name of components that altered this specific entity.

The use of synonyms can also cause the value field not match the text exactly. Instead it will return the trained synonym.